Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have been extensively used in a wide variety of industries such as healthcare and criminal justice. However, the fairness issues in these models have long been criticized, and the use of big data and machine learning algorithms in the construction of scoring systems heightens this concern. In this paper, we propose a general framework to create fairness-aware, data-driven scoring systems. First, we develop a social welfare function that incorporates both efficiency and group fairness. Then, we transform the social welfare maximization problem into the risk minimization task in machine learning, and derive a fairness-aware scoring system with the help of mixed integer programming. Lastly, several theoretical bounds are derived for providing parameter selection suggestions. Our proposed framework provides a suitable solution to address group fairness concerns in the development of scoring systems. It enables policymakers to set and customize their desired fairness requirements as well as other application-specific constraints. We test the proposed algorithm with several empirical data sets. Experimental evidence supports the effectiveness of the proposed scoring system in achieving the optimal welfare of stakeholders and in balancing the needs for interpretability, fairness, and efficiency.
翻译:作为一种预测模型,分级系统在可解释性和透明度方面有很大优势,便于快速决策。因此,评分系统在医疗保健和刑事司法等各种行业广泛使用,但这些模式的公平问题长期以来一直受到批评,在建立评分系统时使用大数据和机器学习算法也加深了这种关切。在本文件中,我们提出了一个创建公平认知、数据驱动的评分系统的一般框架。首先,我们开发了一种社会福利功能,既包括效率和群体公平,又包括效率和群体公平。然后,我们把社会福利最大化问题转化为机器学习中的风险最小化任务,并在混合整数编程帮助下形成一个公平认知的评分系统。最后,为提供参数选择建议,提出了若干理论界限。我们提议的框架为解决评分系统开发中的集团公平问题提供了适当的解决办法。它使决策者能够设定和定制他们所希望的公平要求以及其他具体应用的限制因素。我们用若干经验数据集测试拟议的算法。实验性证据支持拟议的评分系统在实现利益攸关方最佳福利、平衡解释需要方面的效率。